FP&A Data & Analytics Engineer

You will partner with FP&A to modernize finance reporting, forecasting and analytics. You will extract data from SAP S/4HANA, legacy management-reporting systems and Google Cloud Platform (GCP) datasets, transform it into trusted, reconciled finance data products, and deliver Power BI semantic models, dashboards and AI agents that accelerate insight generation and shorten the time from close to outlook. You will also help migrate and industrialize existing Alteryx workflows and spreadsheet-based business logic into governed ELT patterns (dbt and/or D-Form), and implement M365 low-code apps/automations to streamline approval, tracking, and controls.

Agentic AI is a core part of this role. You will design, build and run AI agents that work on governed finance data: answering questions in natural language, reconciling and validating numbers, and drafting variance and close commentary. A finance owner reviews and approves every output before it is used.

This is a hands-on "finance data translator" role. You will combine strong data engineering fundamentals, practical Power BI modeling skills and applied agentic AI engineering with enough finance domain understanding (P&L, cost drivers, allocations and absorbed vs. incurred cost, volume/mix/FX variance walks, accruals, close and forecast cycles) to validate and explain results to FP&A stakeholders and leadership. Accuracy is the product: a number that is wrong but looks plausible is worse than saying it cannot be answered yet.

Data engineering & modeling

• Ingest data from SAP S/4HANA, Oracle/Essbase, flat files and GCP (BigQuery, Cloud Storage) using incremental, partitioned and audited load patterns.
• Migrate Alteryx workflows and spreadsheet logic to governed, version-controlled ELT (dbt and/or D-Form).
• Build curated finance data models (star schemas, hierarchies, multi-scenario/version data) with documented KPIs and lineage.
• Reconcile outputs to source systems and published figures through automated data quality checks.

Power BI & analytics

• Build Power BI semantic models (DAX, row-level security, performance tuning) and manage them as code.
• Deliver dashboards for P&L, forecast vs. prior forecast, forecast accuracy, and volume/cost/FX variance bridges.
• Build driver-based in-month outlooks that test the forecast before leadership reviews.

Agentic AI & automation

• Design, build and run AI agents on governed finance data, for example natural-language Q&A (NL-to-SQL), automated reconciliations, and draft variance and close commentary.
• Connect agents to enterprise systems through APIs and Model Context Protocol (MCP) connectors with least-privilege access.
• Maintain the knowledge base and reusable skills that ground the agents, with evaluation (test sets, tie-outs), guardrails and human-in-the-loop approval.
• Automate finance workflows (submissions, approvals, alerts) with Power Apps, Power Automate, SharePoint and Teams.

Partnership & delivery

• Work with FP&A to translate requirements, validate numbers and explain discrepancies between systems.
• Take proofs of concept to production with SOX-ready controls, documentation and clear ownership.
• Explain technical trade-offs and architecture to leadership in business terms.

Required Qualifications

• 5+ years in data engineering, BI engineering or analytics engineering.
• Hands-on agentic AI experience: has built and deployed an LLM-based agent or workflow that uses tools/APIs on enterprise data (e.g., NL-to-SQL, RAG, MCP), including evaluation and guardrails.
• Strong SQL and Python (pandas, APIs); experience with BigQuery or a similar cloud data warehouse.
• Hands-on Power BI experience: data modeling, DAX, Power Query and performance optimization.
• Experience building production data pipelines with Git, CI/CD and operational controls.
• Exposure to finance analytics (FP&A, controlling, P&L, cost/volume/FX analysis).

Preferred Qualifications

• Alteryx-to-dbt migration experience.
• Agent frameworks (e.g., Vertex AI / Google ADK, LangGraph, Copilot Studio) and building MCP servers.
• SAP S/4HANA finance data, Oracle Essbase/HFM, or management allocation and consolidation systems.
• Forecasting and time-series methods; Python web apps (Dash, Streamlit) or React.
• Familiarity with SOX controls, data governance and Responsible AI practices.